Use BlueStyle (#19)

Author: devmotion

.JuliaFormatter.toml

@@ -0,0 +1 @@
+style="blue"

.github/workflows/Format.yml

@@ -0,0 +1,24 @@
+name: Format
+
+on:
+  pull_request:
+
+jobs:
+  format:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: julia-actions/setup-julia@latest
+        with:
+          version: 1
+      - name: Install JuliaFormatter and format code
+        run: |
+          using Pkg: Pkg
+          Pkg.add(; name="JuliaFormatter", uuid="98e50ef6-434e-11e9-1051-2b60c6c9e899")
+          using JuliaFormatter
+          format("."; verbose=true)
+        shell: julia --color=yes {0}
+      - uses: reviewdog/action-suggester@v1
+        with:
+          tool_name: JuliaFormatter
+          fail_on_error: true

README.md

@@ -6,6 +6,7 @@ Julia implementation of elliptical slice sampling.
 [![Build Status](https://github.com/TuringLang/EllipticalSliceSampling.jl/workflows/CI/badge.svg?branch=main)](https://github.com/TuringLang/EllipticalSliceSampling.jl/actions?query=workflow%3ACI%20branch%3Amain)
 [![Codecov](https://codecov.io/gh/TuringLang/EllipticalSliceSampling.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/TuringLang/EllipticalSliceSampling.jl)
 [![Coveralls](https://coveralls.io/repos/github/TuringLang/EllipticalSliceSampling.jl/badge.svg?branch=main)](https://coveralls.io/github/TuringLang/EllipticalSliceSampling.jl?branch=main)
+[![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle)
 
 ## Overview
 

src/EllipticalSliceSampling.jl

@@ -1,11 +1,11 @@
 module EllipticalSliceSampling
 
-import AbstractMCMC
-import ArrayInterface
-import Distributions
+using AbstractMCMC: AbstractMCMC
+using ArrayInterface: ArrayInterface
+using Distributions: Distributions
 
-import Random
-import Statistics
+using Random: Random
+using Statistics: Statistics
 
 export ESSModel, ESS
 

src/abstractmcmc.jl

@@ -19,10 +19,7 @@ end
 
 # first step of the elliptical slice sampler
 function AbstractMCMC.step(
-    rng::Random.AbstractRNG,
-    model::AbstractMCMC.AbstractModel,
-    ::ESS;
-    kwargs...
+    rng::Random.AbstractRNG, model::AbstractMCMC.AbstractModel, ::ESS; kwargs...
 )
     # initial sample from the Gaussian prior
     f = initial_sample(rng, model)
@@ -39,7 +36,7 @@ function AbstractMCMC.step(
     model::AbstractMCMC.AbstractModel,
     ::ESS,
     state::ESSState;
-    kwargs...
+    kwargs...,
 )
     # obtain the prior
     prior = EllipticalSliceSampling.prior(model)

src/model.jl

@@ -7,14 +7,14 @@ struct ESSModel{P,L} <: AbstractMCMC.AbstractModel
     loglikelihood::L
 
     function ESSModel{P,L}(prior::P, loglikelihood::L) where {P,L}
-        isgaussian(P) ||
-            error("prior distribution has to be a Gaussian distribution")
-        new{P,L}(prior, loglikelihood)
+        isgaussian(P) || error("prior distribution has to be a Gaussian distribution")
+        return new{P,L}(prior, loglikelihood)
     end
 end
 
-ESSModel(prior, loglikelihood) =
-    ESSModel{typeof(prior),typeof(loglikelihood)}(prior, loglikelihood)
+function ESSModel(prior, loglikelihood)
+    return ESSModel{typeof(prior),typeof(loglikelihood)}(prior, loglikelihood)
+end
 
 # obtain prior
 prior(model::ESSModel) = model.prior

test/regression.jl

@@ -1,67 +1,70 @@
 @testset "regression.jl" begin
 
-# number of input features
-N = 200
-# homoscedastic observation noise
-σ = 0.3
+    # number of input features
+    N = 200
+    # homoscedastic observation noise
+    σ = 0.3
 
-# experiment of Murray et al.
-@testset "GP regression" begin
-    # added to the diagonal of the covariance matrix due to numerical issues of
-    # the Cholesky decomposition
-    jitter = 1e-9
+    # experiment of Murray et al.
+    @testset "GP regression" begin
+        # added to the diagonal of the covariance matrix due to numerical issues of
+        # the Cholesky decomposition
+        jitter = 1e-9
 
-    # for different dimensions of input features
-    for d in 1:10
-        # sample input features
-        inputs = rand(d, N)
+        # for different dimensions of input features
+        for d in 1:10
+            # sample input features
+            inputs = rand(d, N)
 
-        # define covariance matrix of latent variable
-        # add noise to the diagonal due to numerical issues
-        prior_Σ = Symmetric(exp.((-0.5) .* pairwise(SqEuclidean(), inputs; dims = 2))) +
-            jitter * I
-        prior = MvNormal(prior_Σ)
+            # define covariance matrix of latent variable
+            # add noise to the diagonal due to numerical issues
+            prior_Σ =
+                Symmetric(exp.((-0.5) .* pairwise(SqEuclidean(), inputs; dims=2))) +
+                jitter * I
+            prior = MvNormal(prior_Σ)
 
-        # sample noisy observations
-        observations = rand(prior) .+ σ .* randn(N)
+            # sample noisy observations
+            observations = rand(prior) .+ σ .* randn(N)
 
-        # define log likelihood function
-        ℓ(f) = let observations = observations, σ = σ
-            logpdf(MvNormal(f, σ), observations)
-        end
+            # define log likelihood function
+            ℓ(f) =
+                let observations = observations, σ = σ
+                    logpdf(MvNormal(f, σ), observations)
+                end
 
-        # run elliptical slice sampler for 100 000 time steps
-        samples = sample(ESSModel(prior, ℓ), ESS(), 100_000; progress = false)
+            # run elliptical slice sampler for 100 000 time steps
+            samples = sample(ESSModel(prior, ℓ), ESS(), 100_000; progress=false)
 
-        # compute analytical posterior of GP
-        posterior_Σ = prior_Σ * (I - (prior_Σ + σ^2 * I) \ prior_Σ)
-        posterior_μ = posterior_Σ * observations / σ^2
+            # compute analytical posterior of GP
+            posterior_Σ = prior_Σ * (I - (prior_Σ + σ^2 * I) \ prior_Σ)
+            posterior_μ = posterior_Σ * observations / σ^2
 
-        # compare with empirical estimates
-        @test mean(samples) ≈ posterior_μ rtol = 0.05
+            # compare with empirical estimates
+            @test mean(samples) ≈ posterior_μ rtol = 0.05
+        end
     end
-end
 
-# extreme case with independent observations
-@testset "Independent components" begin
-    # define  distribution of latent variables
-    prior = MvNormal(N, 1)
+    # extreme case with independent observations
+    @testset "Independent components" begin
+        # define  distribution of latent variables
+        prior = MvNormal(N, 1)
 
-    # sample noisy observations
-    observations = rand(prior) .+ σ .* randn(N)
+        # sample noisy observations
+        observations = rand(prior) .+ σ .* randn(N)
 
-    # define log likelihood function
-    ℓ(f) = let observations = observations, σ = σ
-        logpdf(MvNormal(f, σ), observations)
-    end
+        # define log likelihood function
+        ℓ(f) =
+            let observations = observations, σ = σ
+                logpdf(MvNormal(f, σ), observations)
+            end
 
-    # run elliptical slice sampling for 100 000 time steps
-    samples = sample(ESSModel(prior, ℓ), ESS(), 100_000; progress = false)
+        # run elliptical slice sampling for 100 000 time steps
+        samples = sample(ESSModel(prior, ℓ), ESS(), 100_000; progress=false)
 
-    # compute analytical posterior
-    posterior_μ = observations / (1 + σ^2)
+        # compute analytical posterior
+        posterior_μ = observations / (1 + σ^2)
 
-    # compare with empirical estimates
-    @test mean(samples) ≈ posterior_μ rtol = 0.02
-end
+        # compare with empirical estimates
+        @test mean(samples) ≈ posterior_μ rtol = 0.02
+    end
 end

test/runtests.jl

@@ -21,8 +21,12 @@ end
 
 @testset "EllipticalSliceSampling" begin
     println("Simple tests")
-    @testset "Simple tests" begin include("simple.jl") end
+    @testset "Simple tests" begin
+        include("simple.jl")
+    end
 
     println("GP regression tests")
-    @testset "GP regression tests" begin include("regression.jl") end
+    @testset "GP regression tests" begin
+        include("regression.jl")
+    end
 end